Abstract
In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, it is essential that domain experts are able to understand and reason with their content. In other words, it is important for these reference conceptual models to be cognitively tractable. This paper contributes to this goal by proposing a model clustering technique that leverages the rich semantics of ontology-driven conceptual models (ODCM). In particular, the technique employs the notion of Relational Context to guide automated model breakdown. Such Relational Contexts capture all the information needed for understanding entities “qua players of roles” in the scope of an objectified (reified) relationship (relator).
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Notes
- 1.
There is a long debate in philosophy regarding the ontological neutrality (or lack thereof) of formal languages. We simply mean here that they commit to a simple ontology of formal structures (e.g., that of set theory) in which sorts of types and relations are undifferentiated.
- 2.
This model consisted of 3,800 classes, 61 datatypes, 1,918 associations, 3,616 subtyping relations, 698 generalization sets, 865 attributes, i.e., navigable association ends [21].
- 3.
The model of Fig. 1 is used here for illustration purposes only, as it is a much simplified version of a proper model in this domain. For example, in a more realistic model, we would have cases of “relators mediating relators” (e.g., a car rental mediating a car ownership and an employment). The example avoids these for the sake of space limitations. Our formal definition of RCC (see Sect. 4.7), however, has no such a limitation, thus, addressing these cases that result in nested contexts (i.e., contexts including other contexts).
- 4.
See source code at https://github.com/OntoUML/ontouml-js.
- 5.
See source code at https://github.com/OntoUML/ontouml-vp-plugin.
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Acknowledgments
We are grateful to Ricardo A. Falbo (in memoriam) for the spark that led to this investigation. This research is partially funded by the NeXON Project (UNIBZ). J.P. Almeida is funded by CAPES (grant number 23038.028816/2016-41) and CNPq (grants numbers 312123/2017-5 and 407235/2017-5).
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Guizzardi, G., Prince Sales, T., Almeida, J.P.A., Poels, G. (2020). Relational Contexts and Conceptual Model Clustering. In: Grabis, J., Bork, D. (eds) The Practice of Enterprise Modeling. PoEM 2020. Lecture Notes in Business Information Processing, vol 400. Springer, Cham. https://doi.org/10.1007/978-3-030-63479-7_15
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